Sentence Similarity
sentence-transformers
PyTorch
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:2400
loss:TripletLoss
loss:MultipleNegativesRankingLoss
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use ostoveland/test13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ostoveland/test13 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ostoveland/test13") sentences = [ "Flislegging av hall", "query: tapetsering av rom med grunnflate 4x4.5 meter minus tre dører", "query: fliser i hall", "query: fornye markiseduk" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download sentence_bert_config.json from ostoveland/test13: direct link, hf CLI and curl.
- Browser
- Download file 53 Bytes
-
https://huggingface.co/ostoveland/test13/resolve/refs%2Fpr%2F1/sentence_bert_config.json
- Command line
-
hf download hf://ostoveland/test13@refs/pr/1/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/ostoveland/test13/resolve/refs%2Fpr%2F1/sentence_bert_config.json
53 Bytes
| { | |
| "max_seq_length": 512, | |
| "do_lower_case": false | |
| } |